Abstract:
—Clustering is one of the widely used techniques to
find out patterns from a dataset that can be applied in different
applications or analyses. K-means, the most popular and simple
clustering algorithm, might get trapped into local minima if not
properly initialized and the initialization of this algorithm is done
randomly. In this paper, we propose a novel approach to improve
initial cluster selection for K-means algorithm. This algorithm
is based on the fact that the initial centroids must be well
separated from each other since the final clusters are separated
groups in feature space. The Convex Hull algorithm facilitates
the computing of the first two centroids and the remaining ones
are selected according to the distance from previously selected
centers. To ensure the selection of one center per cluster, we
use the nearest neighbor technique. To check the robustness of
our proposed algorithm, we consider several real-world datasets.
We obtained only 7.33%, 7.90%, and 0% clustering error in
Iris, Letter, and Ruspini data respectively which proves better
performance than other existing systems. The results indicate
that our proposed method outperforms the conventional K means
approach by accelerating the computation when the number of
clusters is greater than 2.
